THIS IS A EXPERIMENTAL DETECTION
This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.
This search looks for suspicious processes on all systems labeled as web servers.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2019-04-01
- Author: David Dorsey, Splunk
- ID: ec3b7601-689a-4463-94e0-c9f45638efb9
Kill Chain Phase
- Actions on Objectives
- CIS 3
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `web_servers_executing_suspicious_processes_filter`
The SPL above uses the following Macros:
web_servers_executing_suspicious_processes_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security.
Known False Positives
Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks.
Associated Analytic Story
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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